{"title":"考虑可调负载充电特性的有源分布式网络动态等效建模","authors":"Jingwen Wang, Jiehui Zheng, Zhigang Li, Qing-Hua Wu","doi":"10.1049/gtd2.13344","DOIUrl":null,"url":null,"abstract":"<p>As more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four-layer, tri-stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log-cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"18 24","pages":"4154-4167"},"PeriodicalIF":2.0000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13344","citationCount":"0","resultStr":"{\"title\":\"Dynamic equivalent modelling for active distributed network considering adjustable loads charging characteristics\",\"authors\":\"Jingwen Wang, Jiehui Zheng, Zhigang Li, Qing-Hua Wu\",\"doi\":\"10.1049/gtd2.13344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>As more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four-layer, tri-stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log-cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.</p>\",\"PeriodicalId\":13261,\"journal\":{\"name\":\"Iet Generation Transmission & Distribution\",\"volume\":\"18 24\",\"pages\":\"4154-4167\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.13344\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iet Generation Transmission & Distribution\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13344\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Generation Transmission & Distribution","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.13344","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Dynamic equivalent modelling for active distributed network considering adjustable loads charging characteristics
As more renewable energy generators and adjustable loads such as electric vehicles are being connected to the power grids, load modelling of the distribution network becomes more complicated. Therefore, this paper explores a dynamic equivalent modelling method for active distribution network that takes into account electric vehicle charging. First of all the combination of integrated ZIP loads and motors is adopted as an equivalent model for active distribution networks. Subsequently, a four-layer, tri-stage deep reinforcement learning approach is used to solve the relevant key parameters of the proposed equivalent model. The method proposed in this paper fully utilizes the superiority of reinforcement learning in decision making, while the method combines the excellent feature extraction capability of deep learning. The method utilizes measurements obtained at boundary nodes to obtain an active distributed network equivalent model after a series of calculations. At the same time, adjustable loads are identified in detail. On the other hand, this method introduces a prioritized empirical playback mechanism, log-cosh loss function, and adaptive operator to improve the computational efficiency of the method. From the simulation results, the present method is effective.
期刊介绍:
IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix.
The scope of IET Generation, Transmission & Distribution includes the following:
Design of transmission and distribution systems
Operation and control of power generation
Power system management, planning and economics
Power system operation, protection and control
Power system measurement and modelling
Computer applications and computational intelligence in power flexible AC or DC transmission systems
Special Issues. Current Call for papers:
Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf